Therapeutic targeting of white adipose tissue metabolic dysfunction in obesity: mechanisms and opportunities
Bibliographic record
Abstract
White adipose tissue is not only a highly heterogeneous organ containing various cells, such as adipocytes, adipose stem and progenitor cells, and immune cells, but also an endocrine organ that is highly important for regulating metabolic and immune homeostasis. In individuals with obesity, dynamic cellular changes in adipose tissue result in phenotypic switching and adipose tissue dysfunction, including pathological expansion, WAT fibrosis, immune cell infiltration, endoplasmic reticulum stress, and ectopic lipid accumulation, ultimately leading to chronic low-grade inflammation and insulin resistance. Recently, many distinct subpopulations of adipose tissue have been identified, providing new insights into the potential mechanisms of adipose dysfunction in individuals with obesity. Therefore, targeting white adipose tissue as a therapeutic agent for treating obesity and obesity-related metabolic diseases is of great scientific interest. Here, we provide an overview of white adipose tissue remodeling in individuals with obesity including cellular changes and discuss the underlying regulatory mechanisms of white adipose tissue metabolic dysfunction. Currently, various studies have uncovered promising targets and strategies for obesity treatment. We also outline the potential therapeutic signaling pathways of targeting adipose tissue and summarize existing therapeutic strategies for antiobesity treatment including pharmacological approaches, lifestyle interventions, and novel therapies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".